A system for predicting a road surface friction coefficient includes an external information acquisition part, a tire information acquisition part, and a friction coefficient prediction part. The external information acquisition part configured to acquire external information on a disturbance factor that affects a condition of a road surface on which a target vehicle travels. The tire information acquisition part configured to acquire tire information indicating a condition of a tire of the target vehicle. The friction coefficient prediction part configured to predict a friction coefficient between the tire and the road surface based on the tire information acquired by the tire information acquisition part and the external information acquired by the external information acquisition part. The friction coefficient prediction part predicts a friction coefficient of a road surface ahead of the target vehicle in a traveling direction.
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1. A system for predicting a friction coefficient, comprising: an external information acquisition part configured to acquire external information on a disturbance factor that affects a condition of a road surface on which a target vehicle travels; a tire information acquisition part configured to acquire tire information indicating a condition of a tire of the target vehicle; and a friction coefficient prediction part configured to predict the friction coefficient between the tire and the road surface based on the tire information acquired by the tire information acquisition part and the external information acquired by the external information acquisition part, wherein the friction coefficient prediction part predicts the friction coefficient of the road surface ahead of the target vehicle in a traveling direction, and wherein the friction coefficient prediction part predicts the friction coefficient of the road surface at a stop position of the target vehicle based on the external information acquired immediately before start of the target vehicle.
This invention relates to a system for predicting the friction coefficient between a vehicle's tires and the road surface, addressing the challenge of accurately estimating road conditions to improve vehicle safety and control. The system includes three main components: an external information acquisition part, a tire information acquisition part, and a friction coefficient prediction part. The external information acquisition part collects data on disturbance factors affecting road surface conditions, such as weather, temperature, or road surface contaminants. The tire information acquisition part gathers data on the tire's condition, including wear, pressure, and temperature. The friction coefficient prediction part uses both sets of data to estimate the friction coefficient between the tire and the road surface. The system predicts friction coefficients for road surfaces ahead of the vehicle in its traveling direction, allowing for proactive adjustments to driving behavior. Additionally, it predicts the friction coefficient at the vehicle's stop position based on external information acquired immediately before the vehicle starts moving, ensuring accurate estimates for parking or stationary conditions. This approach enhances vehicle stability and safety by providing real-time and predictive friction data.
2. The system according to claim 1 , wherein the external information includes at least one of weather information and road information.
This invention relates to a system for enhancing vehicle navigation or autonomous driving by integrating external information to improve route planning, safety, or efficiency. The system collects and processes data from external sources, such as weather conditions or road status, to adjust navigation decisions dynamically. Weather information may include precipitation, temperature, or visibility, while road information may cover traffic congestion, construction zones, or road closures. By analyzing this data, the system can modify routes to avoid hazardous conditions, optimize fuel efficiency, or reduce travel time. The system may also prioritize certain types of external information based on relevance or urgency, ensuring that critical factors like severe weather or accidents are given higher weight in decision-making. The integration of real-time external data allows the system to provide more accurate and context-aware navigation solutions compared to traditional systems that rely solely on static maps or basic traffic updates. This approach enhances safety, efficiency, and user experience for both human drivers and autonomous vehicles.
3. The system according to claim 1 , wherein the tire information acquisition part acquires the tire information based on friction coefficients between tires of a plurality of preceding vehicles and road surfaces.
The system is designed for vehicle control and safety, focusing on tire performance and road conditions. It addresses the challenge of accurately assessing tire behavior to improve vehicle stability and safety, particularly in varying road conditions. The system includes a tire information acquisition part that collects data on tire performance, specifically by analyzing friction coefficients between the tires of multiple preceding vehicles and the road surface. This data helps determine the road's grip and tire wear, enabling real-time adjustments to vehicle control systems. The system also incorporates a tire information processing part that processes the acquired data to generate actionable insights, such as adjusting braking, steering, or traction control. Additionally, a vehicle control part uses this processed information to optimize vehicle dynamics, ensuring safer and more efficient operation. By leveraging data from multiple preceding vehicles, the system provides a more comprehensive and accurate assessment of road conditions, enhancing overall vehicle safety and performance. The invention improves upon existing systems by incorporating real-time, multi-vehicle data to dynamically adapt to changing road conditions.
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May 26, 2020
March 15, 2022
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